Overview

Dataset statistics

Number of variables12
Number of observations244
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory62.2 KiB
Average record size in memory260.9 B

Variable types

Numeric9
Categorical3

Alerts

Temperature is highly correlated with RH and 5 other fieldsHigh correlation
RH is highly correlated with Temperature and 5 other fieldsHigh correlation
FFMC is highly correlated with Temperature and 7 other fieldsHigh correlation
DMC is highly correlated with Rain and 6 other fieldsHigh correlation
DC is highly correlated with Rain and 5 other fieldsHigh correlation
ISI is highly correlated with Temperature and 8 other fieldsHigh correlation
BUI is highly correlated with Rain and 6 other fieldsHigh correlation
FWI is highly correlated with Temperature and 8 other fieldsHigh correlation
Classes is highly correlated with Temperature and 8 other fieldsHigh correlation
Rain is highly correlated with FFMC and 6 other fieldsHigh correlation
Ws is highly correlated with TemperatureHigh correlation
region is highly correlated with RHHigh correlation
region is uniformly distributed Uniform
ISI has 4 (1.6%) zeros Zeros
FWI has 9 (3.7%) zeros Zeros

Reproduction

Analysis started2023-05-01 10:22:06.740484
Analysis finished2023-05-01 10:22:15.694430
Duration8.95 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Temperature
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.17213115
Minimum22
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-05-01T15:52:15.746663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile26
Q130
median32
Q335
95-th percentile37.85
Maximum42
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.63384326
Coefficient of variation (CV)0.1129500325
Kurtosis-0.1543103757
Mean32.17213115
Median Absolute Deviation (MAD)3
Skewness-0.1963088795
Sum7850
Variance13.20481684
MonotonicityNot monotonic
2023-05-01T15:52:15.855192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3529
11.9%
3125
10.2%
3424
9.8%
3323
9.4%
3022
9.0%
3221
8.6%
3621
8.6%
2918
7.4%
2815
6.1%
379
 
3.7%
Other values (9)37
15.2%
ValueCountFrequency (%)
222
 
0.8%
243
 
1.2%
256
 
2.5%
265
 
2.0%
278
 
3.3%
2815
6.1%
2918
7.4%
3022
9.0%
3125
10.2%
3221
8.6%
ValueCountFrequency (%)
421
 
0.4%
403
 
1.2%
396
 
2.5%
383
 
1.2%
379
 
3.7%
3621
8.6%
3529
11.9%
3424
9.8%
3323
9.4%
3221
8.6%

RH
Real number (ℝ≥0)

HIGH CORRELATION

Distinct62
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.93852459
Minimum21
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-05-01T15:52:16.522201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile37
Q152
median63
Q373.25
95-th percentile86
Maximum90
Range69
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation14.88420018
Coefficient of variation (CV)0.2403060176
Kurtosis-0.5303278714
Mean61.93852459
Median Absolute Deviation (MAD)11
Skewness-0.2379643933
Sum15113
Variance221.5394151
MonotonicityNot monotonic
2023-05-01T15:52:16.660614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6410
 
4.1%
5510
 
4.1%
588
 
3.3%
548
 
3.3%
788
 
3.3%
687
 
2.9%
667
 
2.9%
737
 
2.9%
807
 
2.9%
657
 
2.9%
Other values (52)165
67.6%
ValueCountFrequency (%)
211
 
0.4%
241
 
0.4%
261
 
0.4%
291
 
0.4%
311
 
0.4%
332
0.8%
343
1.2%
351
 
0.4%
361
 
0.4%
374
1.6%
ValueCountFrequency (%)
901
 
0.4%
893
1.2%
883
1.2%
874
1.6%
863
1.2%
842
 
0.8%
831
 
0.4%
823
1.2%
816
2.5%
807
2.9%

Ws
Real number (ℝ≥0)

HIGH CORRELATION

Distinct18
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.50409836
Minimum6
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-05-01T15:52:16.797315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q114
median15
Q317
95-th percentile20
Maximum29
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.810178371
Coefficient of variation (CV)0.181253905
Kurtosis2.602155825
Mean15.50409836
Median Absolute Deviation (MAD)2
Skewness0.5458812499
Sum3783
Variance7.897102476
MonotonicityNot monotonic
2023-05-01T15:52:16.951782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1443
17.6%
1540
16.4%
1330
12.3%
1728
11.5%
1627
11.1%
1826
10.7%
1915
 
6.1%
218
 
3.3%
117
 
2.9%
127
 
2.9%
Other values (8)13
 
5.3%
ValueCountFrequency (%)
61
 
0.4%
81
 
0.4%
92
 
0.8%
103
 
1.2%
117
 
2.9%
127
 
2.9%
1330
12.3%
1443
17.6%
1540
16.4%
1627
11.1%
ValueCountFrequency (%)
291
 
0.4%
261
 
0.4%
222
 
0.8%
218
 
3.3%
202
 
0.8%
1915
 
6.1%
1826
10.7%
1728
11.5%
1627
11.1%
1540
16.4%

Rain
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size15.2 KiB
not rain
133 
rain
111 

Length

Max length8
Median length8
Mean length6.180327869
Min length4

Characters and Unicode

Total characters1508
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot rain
2nd rowrain
3rd rowrain
4th rowrain
5th rownot rain

Common Values

ValueCountFrequency (%)
not rain133
54.5%
rain111
45.5%

Length

2023-05-01T15:52:17.082813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-05-01T15:52:17.211153image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
rain244
64.7%
not133
35.3%

Most occurring characters

ValueCountFrequency (%)
n377
25.0%
r244
16.2%
a244
16.2%
i244
16.2%
o133
 
8.8%
t133
 
8.8%
133
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1375
91.2%
Space Separator133
 
8.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n377
27.4%
r244
17.7%
a244
17.7%
i244
17.7%
o133
 
9.7%
t133
 
9.7%
Space Separator
ValueCountFrequency (%)
133
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1375
91.2%
Common133
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
n377
27.4%
r244
17.7%
a244
17.7%
i244
17.7%
o133
 
9.7%
t133
 
9.7%
Common
ValueCountFrequency (%)
133
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n377
25.0%
r244
16.2%
a244
16.2%
i244
16.2%
o133
 
8.8%
t133
 
8.8%
133
 
8.8%

FFMC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct173
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.88770492
Minimum28.6
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-05-01T15:52:17.310221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum28.6
5-th percentile47.145
Q172.075
median83.5
Q388.3
95-th percentile92.185
Maximum96
Range67.4
Interquartile range (IQR)16.225

Descriptive statistics

Standard deviation14.33757088
Coefficient of variation (CV)0.1840800277
Kurtosis1.05520829
Mean77.88770492
Median Absolute Deviation (MAD)5.7
Skewness-1.325633262
Sum19004.6
Variance205.5659387
MonotonicityNot monotonic
2023-05-01T15:52:17.436647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.98
 
3.3%
89.45
 
2.0%
89.34
 
1.6%
85.44
 
1.6%
89.14
 
1.6%
78.33
 
1.2%
88.13
 
1.2%
88.33
 
1.2%
47.43
 
1.2%
79.93
 
1.2%
Other values (163)204
83.6%
ValueCountFrequency (%)
28.61
0.4%
30.51
0.4%
36.11
0.4%
37.31
0.4%
37.91
0.4%
40.91
0.4%
41.11
0.4%
42.61
0.4%
44.91
0.4%
451
0.4%
ValueCountFrequency (%)
961
0.4%
94.31
0.4%
94.21
0.4%
93.92
0.8%
93.81
0.4%
93.71
0.4%
93.31
0.4%
931
0.4%
92.52
0.8%
92.22
0.8%

DMC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct166
Distinct (%)68.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.67336066
Minimum0.7
Maximum65.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-05-01T15:52:17.572595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile1.9
Q15.8
median11.3
Q320.75
95-th percentile41.01
Maximum65.9
Range65.2
Interquartile range (IQR)14.95

Descriptive statistics

Standard deviation12.36803873
Coefficient of variation (CV)0.8428906658
Kurtosis2.487598085
Mean14.67336066
Median Absolute Deviation (MAD)6.9
Skewness1.527652386
Sum3580.3
Variance152.9683821
MonotonicityNot monotonic
2023-05-01T15:52:17.707828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.95
 
2.0%
12.54
 
1.6%
1.94
 
1.6%
3.43
 
1.2%
4.63
 
1.2%
163
 
1.2%
63
 
1.2%
3.23
 
1.2%
9.73
 
1.2%
2.63
 
1.2%
Other values (156)210
86.1%
ValueCountFrequency (%)
0.71
 
0.4%
0.92
0.8%
1.12
0.8%
1.21
 
0.4%
1.33
1.2%
1.71
 
0.4%
1.94
1.6%
2.11
 
0.4%
2.22
0.8%
2.41
 
0.4%
ValueCountFrequency (%)
65.91
0.4%
61.31
0.4%
56.31
0.4%
54.21
0.4%
51.31
0.4%
50.21
0.4%
471
0.4%
46.61
0.4%
46.11
0.4%
45.61
0.4%

DC
Real number (ℝ≥0)

HIGH CORRELATION

Distinct198
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.28811475
Minimum6.9
Maximum220.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-05-01T15:52:17.863758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile7.6
Q113.275
median33.1
Q368.15
95-th percentile158.86
Maximum220.4
Range213.5
Interquartile range (IQR)54.875

Descriptive statistics

Standard deviation47.61966238
Coefficient of variation (CV)0.9661489918
Kurtosis1.614097336
Mean49.28811475
Median Absolute Deviation (MAD)23.9
Skewness1.479041666
Sum12026.3
Variance2267.632245
MonotonicityNot monotonic
2023-05-01T15:52:17.999202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85
 
2.0%
7.64
 
1.6%
7.84
 
1.6%
8.44
 
1.6%
7.54
 
1.6%
8.34
 
1.6%
8.24
 
1.6%
173
 
1.2%
16.62
 
0.8%
102
 
0.8%
Other values (188)208
85.2%
ValueCountFrequency (%)
6.91
 
0.4%
72
0.8%
7.11
 
0.4%
7.32
0.8%
7.42
0.8%
7.54
1.6%
7.64
1.6%
7.72
0.8%
7.84
1.6%
7.91
 
0.4%
ValueCountFrequency (%)
220.41
0.4%
210.41
0.4%
200.21
0.4%
190.61
0.4%
181.31
0.4%
180.41
0.4%
177.31
0.4%
171.31
0.4%
168.21
0.4%
167.21
0.4%

ISI
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct106
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.759836066
Minimum0
Maximum19
Zeros4
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-05-01T15:52:18.128247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q11.4
median3.5
Q37.3
95-th percentile13.37
Maximum19
Range19
Interquartile range (IQR)5.9

Descriptive statistics

Standard deviation4.15462774
Coefficient of variation (CV)0.8728510148
Kurtosis0.8298604263
Mean4.759836066
Median Absolute Deviation (MAD)2.4
Skewness1.126950083
Sum1161.4
Variance17.26093166
MonotonicityNot monotonic
2023-05-01T15:52:18.259504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.18
 
3.3%
1.27
 
2.9%
5.25
 
2.0%
1.55
 
2.0%
2.85
 
2.0%
4.75
 
2.0%
5.65
 
2.0%
0.45
 
2.0%
15
 
2.0%
1.44
 
1.6%
Other values (96)190
77.9%
ValueCountFrequency (%)
04
1.6%
0.14
1.6%
0.24
1.6%
0.33
1.2%
0.45
2.0%
0.52
 
0.8%
0.64
1.6%
0.74
1.6%
0.83
1.2%
0.92
 
0.8%
ValueCountFrequency (%)
191
0.4%
18.51
0.4%
17.21
0.4%
16.61
0.4%
161
0.4%
15.72
0.8%
15.51
0.4%
14.31
0.4%
14.21
0.4%
13.82
0.8%

BUI
Real number (ℝ≥0)

HIGH CORRELATION

Distinct173
Distinct (%)70.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.67336066
Minimum1.1
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-05-01T15:52:18.399401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2.43
Q16
median12.45
Q322.525
95-th percentile46.35
Maximum68
Range66.9
Interquartile range (IQR)16.525

Descriptive statistics

Standard deviation14.20164844
Coefficient of variation (CV)0.8517568071
Kurtosis1.979913218
Mean16.67336066
Median Absolute Deviation (MAD)7.35
Skewness1.458466015
Sum4068.3
Variance201.6868183
MonotonicityNot monotonic
2023-05-01T15:52:18.531024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35
 
2.0%
5.14
 
1.6%
14.23
 
1.2%
2.93
 
1.2%
11.53
 
1.2%
8.33
 
1.2%
2.43
 
1.2%
7.73
 
1.2%
14.13
 
1.2%
4.43
 
1.2%
Other values (163)211
86.5%
ValueCountFrequency (%)
1.11
 
0.4%
1.42
0.8%
1.62
0.8%
1.72
0.8%
1.82
0.8%
2.21
 
0.4%
2.43
1.2%
2.62
0.8%
2.72
0.8%
2.82
0.8%
ValueCountFrequency (%)
681
0.4%
67.41
0.4%
641
0.4%
62.91
0.4%
59.51
0.4%
59.31
0.4%
57.11
0.4%
54.91
0.4%
54.71
0.4%
50.91
0.4%

FWI
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct126
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.049180328
Minimum0
Maximum31.1
Zeros9
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-05-01T15:52:18.670322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.7
median4.45
Q311.375
95-th percentile21.495
Maximum31.1
Range31.1
Interquartile range (IQR)10.675

Descriptive statistics

Standard deviation7.428365676
Coefficient of variation (CV)1.05379141
Kurtosis0.6553162089
Mean7.049180328
Median Absolute Deviation (MAD)4.05
Skewness1.143242624
Sum1720
Variance55.18061661
MonotonicityNot monotonic
2023-05-01T15:52:18.796425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.412
 
4.9%
0.810
 
4.1%
0.59
 
3.7%
0.19
 
3.7%
09
 
3.7%
0.38
 
3.3%
0.97
 
2.9%
0.26
 
2.5%
0.75
 
2.0%
0.64
 
1.6%
Other values (116)165
67.6%
ValueCountFrequency (%)
09
3.7%
0.19
3.7%
0.26
2.5%
0.38
3.3%
0.412
4.9%
0.59
3.7%
0.64
 
1.6%
0.75
2.0%
0.810
4.1%
0.97
2.9%
ValueCountFrequency (%)
31.11
0.4%
30.31
0.4%
30.21
0.4%
301
0.4%
26.91
0.4%
26.31
0.4%
26.11
0.4%
25.41
0.4%
24.51
0.4%
241
0.4%

Classes
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size15.1 KiB
fire
138 
not fire
106 

Length

Max length8
Median length4
Mean length5.737704918
Min length4

Characters and Unicode

Total characters1400
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot fire
2nd rownot fire
3rd rownot fire
4th rownot fire
5th rownot fire

Common Values

ValueCountFrequency (%)
fire138
56.6%
not fire106
43.4%

Length

2023-05-01T15:52:18.928907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-05-01T15:52:19.048620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fire244
69.7%
not106
30.3%

Most occurring characters

ValueCountFrequency (%)
f244
17.4%
i244
17.4%
r244
17.4%
e244
17.4%
n106
7.6%
o106
7.6%
t106
7.6%
106
7.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1294
92.4%
Space Separator106
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f244
18.9%
i244
18.9%
r244
18.9%
e244
18.9%
n106
8.2%
o106
8.2%
t106
8.2%
Space Separator
ValueCountFrequency (%)
106
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1294
92.4%
Common106
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
f244
18.9%
i244
18.9%
r244
18.9%
e244
18.9%
n106
8.2%
o106
8.2%
t106
8.2%
Common
ValueCountFrequency (%)
106
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1400
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f244
17.4%
i244
17.4%
r244
17.4%
e244
17.4%
n106
7.6%
o106
7.6%
t106
7.6%
106
7.6%

region
Categorical

HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size15.0 KiB
Bejaia
122 
Abbes
122 

Length

Max length6
Median length5.5
Mean length5.5
Min length5

Characters and Unicode

Total characters1342
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBejaia
2nd rowBejaia
3rd rowBejaia
4th rowBejaia
5th rowBejaia

Common Values

ValueCountFrequency (%)
Bejaia122
50.0%
Abbes122
50.0%

Length

2023-05-01T15:52:19.142926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-05-01T15:52:19.254902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
bejaia122
50.0%
abbes122
50.0%

Most occurring characters

ValueCountFrequency (%)
e244
18.2%
a244
18.2%
b244
18.2%
B122
9.1%
j122
9.1%
i122
9.1%
A122
9.1%
s122
9.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1098
81.8%
Uppercase Letter244
 
18.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e244
22.2%
a244
22.2%
b244
22.2%
j122
11.1%
i122
11.1%
s122
11.1%
Uppercase Letter
ValueCountFrequency (%)
B122
50.0%
A122
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1342
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e244
18.2%
a244
18.2%
b244
18.2%
B122
9.1%
j122
9.1%
i122
9.1%
A122
9.1%
s122
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1342
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e244
18.2%
a244
18.2%
b244
18.2%
B122
9.1%
j122
9.1%
i122
9.1%
A122
9.1%
s122
9.1%

Interactions

2023-05-01T15:52:14.455069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:07.008481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:07.977022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:08.907696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:09.804229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:10.719431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:11.640809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:12.535829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:13.481382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:14.566415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:07.134360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:08.090444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:09.015121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:09.916037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:10.835191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:11.748481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:12.649027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:13.595256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:14.675403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:07.244301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:08.193172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:09.119365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:10.017324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:10.937973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:11.855141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:12.756682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:13.702360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:14.774683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:07.344229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:08.298221image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:09.211913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:10.114466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:11.034389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:11.949770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:12.859731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:13.806225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:14.875978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:07.449090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:08.403317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:09.311805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:10.211489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:11.140080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:12.047765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:12.963339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:13.912040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:14.972209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:07.553081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:08.502545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:09.407722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:10.317420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:11.240284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:12.145076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:13.069274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:14.016520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:15.069415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:07.649202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:08.597413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:09.500156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:10.408906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:11.330594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:12.232727image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:13.162196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:14.110031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:15.173515image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:07.762835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:08.703688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:09.601707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:10.514580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:11.438223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:12.335046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:13.267543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:14.218014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:15.279554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:07.874109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:08.805784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:09.706059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:10.616466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:11.538651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:12.437884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:13.376308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-01T15:52:14.326681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-05-01T15:52:19.333353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-05-01T15:52:19.476223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-05-01T15:52:19.617795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-05-01T15:52:19.752121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-05-01T15:52:19.868097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-05-01T15:52:15.440147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.

Sample

First rows

TemperatureRHWsRainFFMCDMCDCISIBUIFWIClassesregion
029.057.018.0not rain65.73.47.61.33.40.5not fireBejaia
129.061.013.0rain64.44.17.61.03.90.4not fireBejaia
226.082.022.0rain47.12.57.10.32.70.1not fireBejaia
325.089.013.0rain28.61.36.90.01.70.0not fireBejaia
427.077.016.0not rain64.83.014.21.23.90.5not fireBejaia
531.067.014.0not rain82.65.822.23.17.02.5fireBejaia
633.054.013.0not rain88.29.930.56.410.97.2fireBejaia
730.073.015.0not rain86.612.138.35.613.57.1fireBejaia
825.088.013.0rain52.97.938.80.410.50.3not fireBejaia
928.079.012.0not rain73.29.546.31.312.60.9not fireBejaia

Last rows

TemperatureRHWsRainFFMCDMCDCISIBUIFWIClassesregion
23435.034.017.0not rain92.223.697.313.829.421.6fireAbbes
23533.064.013.0not rain88.926.1106.37.132.413.7fireAbbes
23635.056.014.0not rain89.029.4115.67.536.015.2fireAbbes
23726.049.06.0rain61.311.928.10.611.90.4not fireAbbes
23828.070.015.0not rain79.913.836.12.414.13.0not fireAbbes
23930.065.014.0not rain85.416.044.54.516.96.5fireAbbes
24028.087.015.0rain41.16.58.00.16.20.0not fireAbbes
24127.087.029.0rain45.93.57.90.43.40.2not fireAbbes
24224.054.018.0rain79.74.315.21.75.10.7not fireAbbes
24324.064.015.0rain67.33.816.51.24.80.5not fireAbbes